Deep learning for surface scattering data analysis

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Dokumentart: PhDThesis
Date: 2026-04-30
Language: English
Faculty: 7 Mathematisch-Naturwissenschaftliche Fakultät
Department: Physik
Advisor: Schreiber, Frank (Prof. Dr.)
Day of Oral Examination: 2024-05-16
DDC Classifikation: 530 - Physics
Keywords: Streuung , Deep learning
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Die Dissertation ist gesperrt bis zum 30. April 2026 !


X-ray and neutron surface scattering techniques, encompassing a range of methodologies such as reflectometry and grazing incidence diffraction, are invaluable across a wide spectrum of scientific and technological applications. The capabilities of these techniques are being continually expanded due to ongoing enhancements of modern synchrotron and neutron sources. These developments offer increased resolution and sensitivity, alongside unprecedented volumes of data, facilitating a more comprehensive and detailed understanding of surfaces and interfaces. However, the vast volume and complexity of data generated have begun to outpace the capabilities of traditional data processing tools. This trend underscores that data analysis has become a major bottleneck in experimental science, emphasizing the urgent need for advanced data processing strategies to fully harness the potential of surface scattering techniques. A promising solution is the emerging field of deep learning techniques, which is revolutionizing scientific discovery by providing rapid processing of complex, high-dimensional data. Nonetheless, significant modifications are required to apply these existing techniques effectively to surface scattering data. This work focuses on two major surface scattering techniques - specular reflectometry and grazing-incidence wide-angle scattering - and introduces innovative solutions for advancing data analysis through deep learning tools specifically designed for surface scattering data. Firstly, the specular reflectometry technique, essential for studying systems like thin films and layered structures, is examined. Reflectivity data analysis poses significant challenges due to the infamous phase problem, which introduces potential ambiguity into data interpretation. Thus, a comprehensive understanding of the examined sample is crucial for successful analysis, demanding the integration of this experimenter-acquired prior knowledge into data analysis pipelines. The research in this work demonstrates two deep learning methods for enhancing reflectometry analysis using the most widely employed statistical frameworks - maximum likelihood estimation and Bayesian inference. These advanced methods facilitate real-time reflectometry analysis for complex structures with multiple estimated parameters. The combination of probabilistic machine learning with conventional tools offers an unparalleled level of precision and reliability of the solution. This is achieved by introducing a new class of prior-aware deep learning methods, which allows the dynamic setting of prior knowledge, a critical factor in handling the phase problem. The introduced methods can be equally applied to other inverse problems. Secondly, this work discusses the first reported fully automated pipeline for the analysis of grazing-incidence wide-angle scattering, enabling the processing of massive amounts of data in real time. The core of the solution is a modern deep learning object detection technique, tailored to the specifics of the data. In conjunction with traditional data processing tools, the method offers an exhaustive analysis of diffraction data including phase identification, determination of coexisting phases, and lattice parameter refinement. All methods demonstrated herein pave the way for a fast, fully-automated analysis of surface scattering data, proposing new standards for deep learning-based data processing.

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